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In the context of global climate change and urban expansion, urban residents are encountering greater rainstorm waterlogging risk. Quantifying population exposure to rainstorms is an important component of rainstorm waterlogging risk assessments. This study utilized a two-dimensional hydrodynamic model to simulate the inundation water depth and inundation area resulting from rainstorms, with return periods of 5, 10, 50, and 100 years, in the Xiong'an New Area, and overlaid the gridded population data in 2017 and in 2035 under SSP2 to assess the change in population exposure. The results show that the average inundation depth and area increase were from 0.11 m and 207.9 km2 to 0.18 m and 667.2 km2 as the rainstorm return period increased from once in 5 years to once in 100 years. The greatest water depths in the main urban areas were mainly located in the low-lying areas along the Daqing River. The total population exposed to rainstorm waterlogging for the 5-, 10-, 50-, and 100-year return periods was 0.31, 0.37, 0.50, and 0.53 million, respectively, in 2017. However, this is projected to rise significantly by 2035 under SSP2, increasing 2–4-fold compared with that in 2017 for the four return periods. Specifically, the projected population exposure is expected to be 0.7, 1.0, 1.8, and 2.0 million, respectively. The longer the return period, the greater the increase in population exposure. The proportion of the population exposed at the 0.05–0.2 m water depth to the total population exposure decreases as the return periods increases, whereas the proportion changes in the opposite direction at the 0.2–0.6 m and >0.6 m depth intervals. Spatially, high-exposure areas are concentrated in densely populated main urban regions in the Xiong'an New Area. In the future, more attention should be paid to densely populated low-lying areas and extreme recurrence rainstorm events for urban flood-risk management to ensure population safety and sustainable urban development. [ABSTRACT FROM AUTHOR]

At first glance, assessing future landslide‐exposed population appears to be a straightforward task if landslide hazard estimates, climate change, and population projections are available. However, the intersection of landslide hazard with socioeconomic elements may result in significant variation of estimated landslide exposure due to considerable variations in population projections. This study aims to investigate the effects of different sources of population data on the evaluation of landslide‐exposed population in China under four Shared Socioeconomic Pathways (SSPs) scenarios. We utilize multiple global climate models (GCMs) from Coupled Model Intercomparison Project Phase 6 and six high‐resolution spatially explicit static and dynamic population data sets to drive available landslide models. The results indicate an overall rise in landslide hazard projections, with an increase in the potential impact area of 0.4%–2.7% and an increase in the landslide frequency of 4.7%–20.1%, depending on the SSPs scenarios and future periods. However, the likely changes in future landslide exposed population, as modeled by incorporating population data from different sources with landslide hazard, yield divergent outcomes depending on the population data source. Thus, some of the projections depict an increase in future landslide exposure, while others show a clear decrease. The nationwide divergence ranged from −64% to +48%. These divergent findings were mainly attributed to differences in population data and a lesser extent to variations in GCMs. The present findings highlight the need to pay closer attention to the dynamic evolution of the elements at risk and the associated data uncertainties. Plain Language Summary: As climate change continues, extreme rainfall is expected to become more frequent and intense. This can lead to changes in landslide risks and how population is exposed to them. While previous studies have mainly focused on how the landslide likelihood may change in time, few have paid attention to how dynamic socioeconomic factors play a role in determining landslide risk. In this study, we used multiple global climate models and different population data sets to investigate how different sources of population information can affect estimates of future landslide risks in China. Overall, we found that landslide hazard is likely to increase in the future. However, when we incorporated different population data into our models, we found divergent results in terms of the number of people exposed to these phenomena. Depending on the population data used, the estimates ranged from a decrease of about 64% to an increase of 48%. These different outcomes were mainly due to variations in population data we used, although climate models also had an impact. These conflicting estimates highlight the need to consider changes in elements at risk when assessing disaster risks and developing targeted measures to adapt to and mitigate the effects of climate change. Key Points: Future landslide exposure is assessed by combining landslide model with spatially explicit population and global climate model dataPotential impact areas and frequency of landslides in China under climate change are projected to increase by 0.4%–2.7% and 4.7%–20.1%, respectivelyVarying sourced population projections may induce divergent signals (−64% to +48%) of landslide‐exposed population under climate change [ABSTRACT FROM AUTHOR]

Gridded distribution of future economy plays an important role in climate change impact assessment. The trend of the output values of different industries is crucial for a variety of planning and design processes. Under the Shared Socioeconomic Pathways (SSPs) global framework, the multidimensional model and Cobb-Douglas production model with localized population and economic parameters are used to develop the annual provincial population and value-added of primary, secondary and tertiary industries in China from 2020 to 2100. The most recently implemented fertility-promoting and industrial planning policies in China are considered in our projections. We build multiple models to evaluate the impact of different types of land use on the value-added of primary, secondary and tertiary industries and then gridded the projected value-added to a 5′ × 5′ resolution, based on recorded county-level economic statistics and gridded land use. The reliability of estimations is verified against 2011–2019 statistical data and multiple published datasets. The high-resolution economic dataset is expected to contribute greatly to national and regional climate change impact, adaptation, and vulnerability studies. Measurement(s) Population • Value-added of industries • Gridded data Technology Type(s) multidimensional projection model • Cobb-Douglas production model • computational modeling technique Sample Characteristic - Location China [ABSTRACT FROM AUTHOR]

Potential evapotranspiration (PET) is an important factor that needs to be considered in regional water management and allocation; thus, the reasonable estimation of PET is an important topic in hydrometeorology and other related fields. There is evidence that increased CO2 concentration alters the physiological properties of vegetation and thus affects PET. In this study, changes in PET with and without the CO2 effect over China is investigated using seven CMIP6-GCMs outputs under seven shared socioeconomic pathways (SSPs) based scenarios (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0, and SSP5-8.5), as well as the contribution rate of CO2 on PET in different climatic regions. Changes in estimated PET based on modified Penman–Monteith (PM) method that considers the CO2 effect is compared with the traditional PM method to examine how PET quantity varies (differences) between these two approaches. The results show that the PET values estimated by the two methods explored opposite trends in 1961–2014 over entire China; it decreases with consideration of CO2 but increases without consideration of CO2. In the future, overall PET is projected to increase under all scenarios during 2015–2100 for China and its three sub-regions. PET generally tends to grow slower when CO2 is taken into account (modified PM approach), than when it is not (traditional PM method). In terms of differences in the estimated PET by the two methods, the difference between the two adopted methods increased in China and its sub-regions for the 1961–2014 period. In the future, the difference in estimated PET is anticipated to continuously increase under SSP3-7.0 and SSP5-8.5. Spatially, a much greater extent of difference is found in the arid region. Across the arid region, the PET difference is projected to be the highest at 138% in the mid-term (2041–2060) with respect to the 1995–2014 period, whereas it tends to increase slower in the long-term period (2081–2100). Importantly, CO2 is found to be the most dominant factor (−154.2% contribution) to have a great effect on PET changes across the arid region. Our findings suggest that ignorance of CO2 concentration in PET estimation will result in significant overestimation of PET in the arid region. However, consideration of CO2 in PET estimation will be beneficial for formulating strategies on future water resource management and sustainable development at the local scale. [ABSTRACT FROM AUTHOR]

Climate change has substantial impacts on regional hydrology in the major river basins. To figure out such latent hydrological impacts of changing climate, more reliable hydrological simulations are imperative. In this study, we evaluated the impacts of climate change on hydrological regime in the Upper Yangtze River Basin based on four downscaled and bias-corrected Global Climate Model outputs from Coupled Model Intercomparison Project Phase 5 under four Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0, and RCP8.5) driving three hydrological models. Two model evaluation approaches were applied: simple and comprehensive. The comprehensive approach was used to evaluate models in the historical period, optimizing objective function at four gauges, and hydrological models were weighted for impact assessment based on their performance. In such a way, projected streamflow time series are obtained under different emission scenarios. Results show that the annual average discharge is projected to increase by 4.1–10.5% under the RCP scenarios at the end of twenty-first century relative to the reference period (1970–1999). Moreover, the high flow is projected to increase and the low flow to decrease indicating a higher probability of flood and drought occurrence in the basin. The severity of floods and droughts may increase. In comparison with the simple one-site model evaluation approach, the comprehensive method reveals that the anticipated extreme flow events would be less severe, and annual mean discharge slightly lower. The projected results imply that application of the comprehensive model evaluation approach could narrow the simulated spreads of projections significantly, and might provide more credible results. [ABSTRACT FROM AUTHOR]

To quantify climate change impact and difference on basin-scale river runoff under the limiting global warming thresholds of 1.5 and 2.0 ∘ C, this study examined four river basins covering a wide hydroclimatic setting. We analyzed projected climate change in four basins, quantified climate change impact on annual and seasonal runoff based on the Soil Water Assessment Tool, and estimated the uncertainty constrained by the global circulation model (GCM) structure and the representative concentration pathways (RCPs). All statistics for the two river basins (the Shiyang River, SYR, and the Chaobai River, CBR) located in northern China indicated generally warmer and wetter conditions, whereas the two river basins (the Huaihe River, HHR, and the Fujiang River, FJR) located in southern China projected less warming and were inconsistent regarding annual precipitation change. The simulated changes in annual runoff were complex; however, there was no shift in seasonal runoff pattern. The 0.5 ∘ C global warming difference resulted in 0.7 and 0.6 ∘ C warming in basins in northern and southern China, respectively. This led to a projected precipitation increase by about 2 % for the four basins and to a decrease in simulated annual runoff of 8 % and 1 % in the SYR and the HHR, respectively, but to an increase of 4 % in the CBR and the FJR. The uncertainty in projected annual temperature was dominated by the GCMs or the RCPs; however, that of precipitation was constrained mainly by the GCMs. The 0.5 ∘ C difference decreased the uncertainty in the annual precipitation projection and the annual and monthly runoff simulation. [ABSTRACT FROM AUTHOR]

An assessment of the impact of climate change on regional hydrological processes is vital for effective water resources management and planning. This study investigated the potential effects of climate change on water availability, seasonal runoff, flooding, and water stress in the Yellow River and Pearl River basins in China over the next 30 years, using a semi-distributed hydrological model based on a combination of five general circulation models with four Representative Concentration Pathway scenarios and five Shared Socioeconomic Pathways. The results indicated annual mean temperature could rise higher in the Yellow River Basin than the Xijiang River Basin during 2021–2050. Higher risks of small floods and some big floods, but lower risks of rare big floods are projected for both basins. Water scarcity will continually threaten the Yellow River Basin, especially during the dry season and around 2025. In comparison with the effects of climate change, population variation was expected to have a greater impact on water scarcity. A longer and drier dry season is projected for the Pearl River Basin, which could aggravate water stress and saltwater intrusion into the Pearl River delta. Although the present findings have implications for water resource management planning, caution should be observed because of the neglect of reservoir/dam operations and inherent projection uncertainty. [ABSTRACT FROM AUTHOR]

Abstract: Significant quasi-cycles in precipitation time series of 132 climate stations spread over China from 1951 to 2002 have been detected by applying Autocorrelation Spectral Analysis (ASA). By the same method, significant quasi-cycles have also been identified in time series of the potential influencing factors: Southern Oscillation Index (SOI), Sea Surface Temperature (SST) and Sea Level Pressure (SLP). Similarities between some precipitation time series spectra and the spectrum of a potential driving force have been detected; e.g. several series from the Yangtze River''s middle reaches show 3–4-year cycles which are similar to the SOI and several SST signals. These time series have been further investigated by low-pass filtering with the Savitzky–Golay filter and by means of correlation analysis. It is proved that the SOI and the SSTs of the Bay of Bengal are in relatively stable anti-phase. However, no stable link between these signals and the precipitation series in the Yangtze River basin can be detected. Subsequent analyses of 850hPa winds lead to the outcome that certain wind patterns could either suppress or force the SST and SOI teleconnection signals. [Copyright &y& Elsevier]

Abstract: Gridded climate data sets are widely used in the analysis, modeling and forecasting of the consequences of climate change. The objective of this study is to compare the impact of different climate datasets (station vs. gridded) on the parameterization of a hydrological model (developed using SWAT2005) of the River Xiangxi, the largest tributary of Yangtze River in the Hubei part of the Three Gorges Reservoir. Climate data used in this study derive from two sources: point daily observations from the Xingshan meteorological station (STN) and gridded (0.5°×0.5°) monthly observations of the CRU TS3.0 global dataset (CRU) downscaled to daily data using a weather generator. Data from 1970 to 1974 were applied for sensitivity analyses and autocalibration and subsequently validate hindcasts over the period 1976–1986. Despite there being only slight differences in mean annual precipitation (1003mm vs. 1052mm) between STN and CRU, the data differ more in their estimates of the number of rain days (136 vs. 112) and wet days standard deviation (11.75mm vs. 18.49mm). The mean, maximum and minimum temperatures from CRU are all lower than those from STN. SWAT parameter sensitivity analysis results show slight differences in the relative rank of the most sensitive parameters, with the differences mainly caused by the lower temperature and more intensive rainfall in CRU relative to STN. Autocalibrated parameters showed very similar values, except for the surface runoff lag coefficient which is higher for the CRU dataset compared to that derived from the STN dataset. Statistic results for discharge simulated based on CRU compared rather well with that based on STN CRU as evaluated using the standard statistics of the Nash–Sutcliffe efficiency, coefficient of determination, and percent error. The sensitivity analysis and autocalibration tool embedded in SWAT2005 is a powerful utility in hydrological modeling of the River Xiangxi, and the CRU dataset appears to be appropriate for application to hydrological modeling in this case, thus providing a good basis for climate change studies. [Copyright &y& Elsevier]

In this paper, future drought characteristics (frequency, duration and intensity) over China are analysed by using four climate models from CMIP6 under the seven SSP-RCP (shared socioeconomic pathway-representative concentration pathway) scenarios (SSP119, SSP126, SSP434, SSP245, SSP460, SSP370, and SSP585) for three defined periods of 2021–2040 (near-term), 2041–2060 (mid-term) and 2081–2100 (long-term). The corresponding four climate models output from CMIP5 are also used to conduct a comparison analysis between CMIP5 and CMIP6 to address the improvements added to CMIP6 in terms of drought identification. The drought characteristics are identified by applying the standardized precipitation-evapotranspiration index (SPEI) at a 12-month timescale and run theory. The results show that CMIP6 has a robust capability to capture historical (1986–2005) drought characteristics. For the future period of 2021–2040, the decrease in precipitation and increase in potential evapotranspiration will lead to continuous dry conditions in the upper and middle Yangtze River basin and eastern Pearl River basin. Relative to the reference period, drought events will be more frequent and severe with longer durations in the Northwest River basins and middle Yangtze River basin. Furthermore, higher emissions signify a greater increase in drought frequency and intensity in the long-term period. Except for the SSP585 scenario, the lower emission scenario corresponds to the higher drought duration soon and in the mid-21st century (2021–2060). This finding is regarded as a "strange phenomenon", which cannot be detected by the previous CMIP5-based emission scenarios (RCP2.6, RCP4.5 and the unlikely pathway RCP8.5). Therefore, additional "possible future"-based scenarios (SSP119, SSP126, SSP434, SSP245, SSP460, and SSP370) should be included in extreme climate studies, especially for the near future and mid-21st century. Notably, compared with CMIP5, the reduced biases in drought characteristics are more likely associated with improvements in the representation of physical processes in climate models from CMIP6. The results of this study could provide a basis for the development of drought adaptation measures over China. • Latest CMIP6 climate models and seven SSP-RCP scenarios are used to analyze the possible changes in droughts over China. • More robust projections will be presented by CMIP6 than CMIP5, due to the better representations in physical processes. • The higher emission signifies shorter drought duration, lower precipitation and PET in mid-21st century over China. [ABSTRACT FROM AUTHOR]